Welcome to the Machine Learning for Data-Driven Decisions Group at the University of Michigan!

Our current research portfolio focuses on major public health problems – including infectious disease, Alzheimer’s disease and diabetes, among others. We develop and apply state-of-the-art AI and machine learning methods to analyze large longitudinal health datasets. Our work spans several aspects of AI including time-series analysis, reinforcement learning and causal inference. We aim to develop the computational methods needed to help organize, process, and transform data into actionable knowledge with the ultimate goal of improving health.

Latest News

Forthcoming paper in the Journal of Alzheimer’s & Dementia: Translational Research & Clinical Interventions by Donna Tjandra et al.Using electronic health record data, Tjandra et al. developed and validated 1) a cohort discovery tool to identify patients with Alzheimer’s disease, which can facilitate downstream analyses, and 2) a machine-learning model that can predict the onset of Alzheimer’s disease up to 10 years in advance.Read more –>

Jenna Wiens recognized with Sloan Research Fellowship. Prof. Jenna Wiens has been selected for a 2020 Sloan Research Fellowship by the Alfred P. Sloan Foundation for her work harnessing patient data to improve healthcare outcomes.Read More –>

Friday Night AI: AI and COVID-19

Erkin Otles speaks about M-CURES, a machine learning model developed by people from our lab. M-CURES can help clinicians tell which COVID-19 patients are most likely to deteriorate.

How can machine learning impact healthcare?

Prof. Jenna Wiens uses machine learning to make sense of the immense amount of patient data generated by modern hospitals. This can help alleviate physician shortages, physician burnout, and the prevalence of medical errors.